• Smart Media Journal
• /
• v.10 no.1
• /
• pp.116-123
• /
• 2021

Due to the rapid growth of the online fashion market and the resulting expansion of online choices, there is a problem that the seller cannot directly respond to a large number of consumers individually, although consumers are increasingly demanding for more personalized recommendation services. Images are being tagged as a way to meet consumer's personalization needs, but when people tagging, tagging is very subjective for each person, and artificial intelligence tagging has very limited words and does not meet the needs of users. To solve this problem, we designed an algorithm that recognizes the shape, attribute, and emotional information of the product included in the image with AI, and codes this information to represent all the information that the image has with a combination of codes. Through this algorithm, it became possible by acquiring a variety of information possessed by the image in real time, such as the sensibility of the fashion image and the TPO information expressed by the fashion image, which was not possible until now. Based on this information, it is possible to go beyond the stage of analyzing the tastes of consumers and make hyper-personalized clothing recommendations that combine the tastes of consumers with information about trends and TPOs.

• Fashion & Textile Research Journal
• /
• v.23 no.6
• /
• pp.799-809
• /
• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• Journal of the Korea Convergence Society
• /
• v.13 no.4
• /
• pp.63-70
• /
• 2022

Due to the recent changes in the education market and Corona, Consumers have moved to the tutoring and online learning markets, and the large-scale education service franchise business is reorganized into a small-scale franchise, a business model that maximizes the profit structure from a position where sales are important. Recently, a new learning balance model that can provide individualized services from teacher-centered to student-centered, motivate students is required. In this paper, we propose a new mathematical franchise model (K-MODEL) that can improve a company's profit structure while satisfying the recent education trends and consumer needs from the point of view of the franchise. K-MODEL expects franchisor and franchisees to have a stable profit structure by developing differentiated content and services, learning and operating processes, and various programs to improve learning achievement.

• Journal of Industrial Convergence
• /
• v.20 no.3
• /
• pp.41-46
• /
• 2022

This paper proposes a consumer-tailored solution to prevent the forgery and falsification of data by incorporating blockchain technology in the online and offline distribution of agricultural produce. The solution provides customized services to consumers based on an analysis of the data generated from the sales, distribution, and consumption of quality of the produce. It can also ensure the safety and credibility of the produce, and allow producers to identify consumption intent and the flow of distribution. Producers will be able to determine the flow of produce based on the data collected and thus tailor promotional efforts. This is expected to be the fourth industrial revolution in the agricultural produce distribution sector. Utilizing blockchain and big data technology to create integrated record management systems that combine multiple solutions will shape future technology trends. In addition, if eco-friendly certification is acknowledged as a valuable service and can be incorporated into the distribution process, this solution could become a one-stop distribution solution for agricultural produce.

• Fashion & Textile Research Journal
• /
• v.23 no.2
• /
• pp.273-289
• /
• 2021

This study aimed to examine the appearance of heated clothing in relation to fashion trends by analyzing constructive components of clothing using product images and actual products. A total of 91 images of domestic and foreign heated clothing products were collected, and a product analysis conducted with six parameters of item classification, namely, concept and image, silhouette, color, number of heating elements, and heating parts. In addition, an in-depth analysis was carried out with 11 products among them, while focusing on further detailed components of the design and heating system. As a result, the overall exterior design of domestic products has been changed from outdoor clothing to daily clothing reflecting the current design trend. Compared with domestic products, foreign products showed a diverse assortment and a greater number of heating regions per individual item of clothing. The current heating system commonly consists of a heating element, power source, controller board, and wires, although the existence and type of switches differed from product to product. To develop a more efficiently heated clothing to expand the market, the design, ease of use, safety, consumer preference, heating functionality, and durability should be considered. Along with design recommendations for future heated clothing, this study also provides a practical guide to the technical aspects of the design of the components of heated clothing.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.24 no.2
• /
• pp.59-72
• /
• 2022

The online fashion market in the 21st century has shown rapid growth. Against this backdrop, using consumer activity data to provide customized customer services has emerged as a viable business model that draws attention. Algorithm-based personalized recommendation services are a good example. But their application in fashion products has clear limitations. It is not easy to identify consumers' perceptions of the attributes of fashion, which are various, hard to define, and very sensitive to trends. So there is a need to compile data on consumers' underlying awareness and to carry out defined research to increase the utilization of such services in the fashion industry and further engage consumers. This research aims to classify the attributes and types of fashion products and to identify consumers' perceptions of a given situation where a recommendation service is offered. To find out consumers' perceptions of and satisfaction with recommendation services, an online and mobile survey was conducted on women in their 20s and 30s, a group that uses recommendation services frequently. A total of 455 responses were used for analysis. SPSS 28.0 was used, combined with Conjoint Analysis and multiple regression, to analyze data. The study results could provide insights into a better understanding of recommendation services and be used as basic data for companies to identify consumers' preferences and draw up a detailed strategy for market segmentation.

• Journal of Mushroom
• /
• v.20 no.2
• /
• pp.35-42
• /
• 2022

This study aimed to examine the major domestic and foreign regulations related to the production of organic products. The production and consumption of organic products have been expanding due to the increase in consumer demand for safe food, as well as improved certification procedures and industry trends. In case of organic mushrooms, there were 405 certified farms nationwide in 2021, with a cultivation area of 3,886,628 m² and a planned production of 6,011 tons. Jeollanam-do has 221 farms, a cultivation area of 2,923,402 m², and a certification plan for 2,132 tons. Shiitake mushrooms are ranked first with 369 farms, a cultivation area of 3,805,636 m², and a certification plan for 3,576 tons, representing 91% of the farms, 98% of the cultivation area, and 60% of the certification planning.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.47 no.4
• /
• pp.625-645
• /
• 2023

This study examined newspaper articles from 2012 to the first quarter of 2021 to explore how interest in and response to veganism have evolved in the fashion and beauty industries over the past decade. By analyzing keywords and word correlations, we discovered a steady increase in veganism-related articles in both English- and Korean-language newspapers published in Korea, especially since 2019. Since 2012, consumer interest in vegan fashion materials has grown, with fashion and beauty emerging in 2018 as significant vegan-related keywords. As a result, brands have adopted vegan certification systems and introduced vegan product lines, and new vegan brands have emerged. Since 2020, companies have been promoting environmental, social, and governance (ESG) management practices and working toward eco-management that reflects vegan trends in all areas, such as cruelty-free product/packaging materials, brands, policies, and services. It is also notable that fashion/beauty consumers have been more actively starting to adopt eco-friendly lifestyles and participate in vegan-related movements since that time. Our findings offer important insights into the evolution of veganism in Korea and can help researchers and industry practitioners to develop future business strategies in the vegan fashion and beauty industries.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.46 no.6
• /
• pp.1142-1160
• /
• 2022

Text data plays a significant role in understanding and analyzing trends in consumer, business, and social sectors. For text analysis, there must be a corpus that reflects specific domain knowledge. However, in the field of fashion, the professional corpus is insufficient. This study aims to develop a taxonomy and thesaurus that considers the specialty of fashion products. To this end, about 100,000 fashion vocabulary terms were collected by crawling text data from WSGN, Pantone, and online platforms; text subsequently was extracted through preprocessing with Python. The taxonomy was composed of items, silhouettes, details, styles, colors, textiles, and patterns/prints, which are seven attributes of clothes. The corpus was completed through processing synonyms of terms from fashion books such as dictionaries. Finally, 10,294 vocabulary words, including 1,956 standard Korean words, were classified in the taxonomy. All data was then developed into a web dictionary system. Quantitative and qualitative performance tests of the results were conducted through expert reviews. The performance of the thesaurus also was verified by comparing the results of text mining analysis through the previously developed corpus. This study contributes to achieving a text data standard and enables meaningful results of text mining analysis in the fashion field.

• Journal of Digital Convergence
• /
• v.20 no.3
• /
• pp.131-143
• /
• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.